{"id":"W2033008660","doi":"10.1364/oe.21.020376","title":"Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM","year":2013,"lang":"en","type":"article","venue":"Optics Express","topic":"Optical Network Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bit error rate; Amplifier; Estimation theory; QAM; Quadrature amplitude modulation; Propagation of uncertainty; Optics; Optical amplifier; Bandwidth (computing); Ranging; Magnitude (astronomy); Electronic engineering; Algorithm; Telecommunications; Physics; Laser; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005376044,0.0005097926,0.0002731377,0.0004108333,0.0001573188,0.0004394639,0.0002690148,0.000302944,0.0006932618],"category_scores_gemma":[0.002516058,0.0001689847,0.0001013603,0.000275666,0.0002546505,0.0007476924,0.0004211361,0.0003288246,0.0001516657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003229654,"about_ca_system_score_gemma":0.0003154291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007163081,"about_ca_topic_score_gemma":0.001496713,"domain_scores_codex":[0.9996308,0.0000924248,0.00002297491,0.00005338965,0.0001726112,0.00002764153],"domain_scores_gemma":[0.9990344,0.0004228566,0.0002123446,0.0001520701,0.0001598024,0.00001854217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007253699,0.0001353299,0.006118331,0.000173581,0.00005087949,0.00008215407,0.0001508357,0.1238659,0.5652483,0.004732379,0.0003369084,0.2983801],"study_design_scores_gemma":[0.00002784355,0.0002064359,0.004613996,0.00001884808,0.00002029968,0.00008921661,0.00002214663,0.5152547,0.4773498,0.001430427,0.0009206126,0.00004568675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4211637,0.0005048948,0.575895,0.0001965759,0.00004094391,0.00005635992,0.0000832458,0.0005619481,0.001497347],"genre_scores_gemma":[0.8785977,0.0001276119,0.1206277,0.00002359942,0.00001016268,0.00002776226,0.0000485005,0.00002856922,0.0005083136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007163081,"threshold_uncertainty_score":0.002843142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666125228262004,"score_gpt":0.2300941114807145,"score_spread":0.2134328591980945,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}